At a Glance

Name: RAG-Based Song Analysis for Appropriateness Using PyTorch
Price: Free CAD
Rating: 5.0 (3 reviews)
Author: karan_goswami, joseph_santarcangelo, faranak_heidari

Learn to analyze song lyrics for appropriateness using RAG and PyTorch. Apply NLP techniques with BERT to generate embeddings, compute cosine similarity, and assess alignment with child-appropriate themes. This project covers preprocessing, similarity measurements, and t-SNE visualization. Ideal for data science and NLP enthusiasts, it offers hands-on experience in AI-driven text analysis. In just under 1 hour, master embedding-based analysis and create impactful insights.

In today’s digital world, where children have easy access to a vast array of music, ensuring the appropriateness of song lyrics is crucial. Using Retrieval-Augmented Generation (RAG) and PyTorch, this project introduces a structured approach to analyzing song lyrics by measuring their alignment with predefined appropriateness criteria. Through advanced natural language processing techniques with BERT, you’ll preprocess text, generate embeddings, and compute similarity scores to evaluate the semantic meaning of lyrics. This project demonstrates how to leverage embeddings to create actionable insights into content suitability, equipping you with practical AI tools for real-world applications.

Why This Topic Is Important

Understanding the appropriateness of content is crucial in scenarios like content moderation, education, and entertainment platforms. This project showcases how to combine AI-driven embeddings with practical evaluation techniques to assess content based on predefined themes. By completing this project, you will learn how to apply NLP techniques with PyTorch and RAG to measure semantic similarity and identify alignment with child-appropriate themes. The project emphasizes explainability and transparency by visualizing relationships through tools like t-SNE.

A Look at the Project Ahead

In this project, you will analyze song lyrics by aligning them with predefined questions about appropriateness using embeddings and similarity metrics. Learn how to preprocess data, generate embeddings with BERT, and visualize patterns using advanced dimensionality reduction techniques. By following this project, you will gain insights into embedding-based analysis, cosine similarity, and RAG-based workflows.

By the end of the project, you will:

Understand how to preprocess text data for embedding generation using the BERT model.
Compute similarity between embeddings using dot product and cosine similarity to evaluate content alignment.
Visualize high-dimensional data using t-SNE to identify patterns and relationships between questions and song lyrics.
Create reusable functions to streamline embedding generation and similarity computation.

What You'll Need

To successfully complete this project, you’ll need:

A foundational understanding of Python programming and libraries like PyTorch, pandas, and matplotlib.
Basic knowledge of natural language processing (NLP) and machine learning concepts.
A web browser to run your code and visualize results.

Offered By: IBMSkillsNetwork

RAG-Based Song Analysis for Appropriateness Using PyTorch

At a Glance

Why This Topic Is Important

A Look at the Project Ahead

What You'll Need